Driver condition determination apparatus, method and computer program product

ABSTRACT

A driver state determination apparatus includes circuitry configured to recognize whether a driver&#39;s voluntary function works normally and to recognize whether an involuntary function works normally. On condition that the driver&#39;s voluntary function does not work normally, the circuitry is configured to reduce a specified time required to recognize whether the involuntary function works normally.

CROSS-REFERENCE TO RELATED APPLICATION

The present application claims priority to JP 2019-186923, filed Oct.10, 2019, the entire contents of which are incorporated herein byreference.

TECHNICAL FIELD

A technique disclosed herein relates to determination of a state of adriver who drives a vehicle, e.g., an automobile.

BACKGROUND ART

Recently, development of automated driving systems has been promoted.

The present applicant considers that there are two types of automateddriving systems.

A first type relates to a system that helps an automobile to transportan occupant to a destination without a need for an operation by adriver, i.e., fully-automated travel of the automobile. For example,Patent document 1 discloses an automated driving technique that shiftsprimary driving responsibility to the automobile when the occupantperforms a specified operation.

The second type relates to an automated driving system that is designedto “provide environment that makes automobile driving enjoyable” thatis, with an assumption that the driver is responsible for driving. Inthe automated driving system of the second type, when a situation occursthat the driver is no longer able to drive normally, e.g., suddenlysuffers from an illness, falls asleep, and the like, the automobileexecutes automated driving instead of the occupant.

Patent document 2 discloses a technique of determining appropriatenessof a vehicle driving state by the driver on the basis of a drivingposture and an eye-opening amount of the driver that are acquired froman analysis result of a video captured by an imaging section. Patentdocument 3 discloses a technique of setting a determination criterionfor a degree of consciousness of the driver and determining a consciousstate of the driver on the basis of detected behavior of the driver'shead and the set determination criterion. In Non-Patent document 1, acase where the driver had a conscious disturbance attack while drivingis discussed.

PRIOR ART DOCUMENTS Patent Documents

-   [Patent document 1] JP-A-2019-119373-   [Patent document 2] JP-A-2019-79328-   [Patent document 3] JP-A-2010-128649

Non-Patent Documents

-   [Non-Patent document 1] Shinohara and 7 others (2014) Untenchu ni    ishiki shogai Kossa wo hassho shita shorei no kentou [Conscious    disturbance attack while driving]. Transactions of Society of    Automotive Engineers of Japan, 45(6), 1105-1110.

SUMMARY OF THE DISCLOSURE

For example, as disclosed in Patent document 2, the following techniquehas been known. In the case where the driver becomes no longer able todrive and hunches over, it is determined that there is a highprobability that the driver suffers from dysfunction or the illness andthus the driving state of the driver is inappropriate. For example, inPatent document 3, motion of the head or a body of the driver isrecognized. Then, based on a recognition result, a so-called dead mandetermination is made. In the case where a state of the driver who canno longer continue driving is recognized, instead of the driver, thetechnique allows the host vehicle to be directed to a safe place.

In such a case, it is extremely important to discover occurrence ofabnormality to the driver, in particular, an outbreak of the dysfunctionor the illness to the driver as soon as possible from perspectives ofimprovement in a life-saving rate of the driver and safety ofsurrounding environment. In particular, in the second type of automateddriving system, the driver is primarily responsible for driving. Thus,it is extremely important to discover the abnormality of the driver assoon as possible in order to provide an ease of mind and safety to thedriver himself/herself and others.

A technique disclosed herein has been made in view of such points andtherefore has a purpose of reducing a time required for an abnormalitydetermination of a driver as much as possible.

One or more embodiments disclosed herein is targeted for a driver statedetermination apparatus that is mounted on an automobile and thatincludes: a voluntary function recognition section that recognizeswhether a voluntary function of a driver works normally in order todetect a prediction of an outbreak of abnormality of the driver; and aninvoluntary function recognition section that recognizes whether aninvoluntary function works normally on the basis of a fact that theinvoluntary function remains in an abnormal state for a specified timeor longer. The driver state determination apparatus is configured toreduce the specified time that is required to recognize whether theinvoluntary function recognition section works normally in the casewhere it is recognized that the voluntary function does not worknormally.

High and low voluntary functions and the recognition of whether theinvoluntary function works normally in the apparatus according toembodiments are directed to saving life and securing safety byaccelerating the abnormality determination of the driver, and includeaspects such as estimation and determinations by apparatus hardware. Inaddition, the recognition in the apparatus according to embodiments is adifferent concept from a concept that a healthcare professional examinesa human body to determine whether the body functions normally.

According to this configuration, in the case where the prediction of theabnormality of the driver is detected on the basis of whether thevoluntary function of the driver works normally and it is recognizedthat the voluntary function of the driver does not work normally, thespecified time that is required to recognize whether the involuntaryfunction recognition section functions normally is reduced. As a result,compared to a case where the abnormality of the driver is determinedonly on the basis of the involuntary function, a time required for thedetermination of the abnormality of the driver can be reduced whileaccuracy of the determination of the abnormality is secured as high aspossible.

As an aspect of the driver state determination apparatus, the voluntaryfunction recognition section may include: a high voluntary functionrecognition section that recognizes whether a high voluntary functionthat is a relatively high voluntary function works normally; and a lowvoluntary function recognition section that recognizes whether a lowvoluntary function that is a lower voluntary function than the highvoluntary function works normally. In the case where it is recognizedthat the high voluntary function recognition section does not functionnormally, determination criteria of the low voluntary functionrecognition section and/or the involuntary function recognition sectionmay be changed. Meanwhile, in the case where it is recognized that thelow voluntary function recognition section does not function normally,the determination criterion of the involuntary function recognitionsection may be changed.

According to this configuration, before the involuntary function isimpaired, a condition for the determination of the abnormality (forexample, a reduction in a determination time) is changed according to acombination of normality/abnormality of the high voluntary function andthe low voluntary function. As a result, it is possible to furtheraccelerate the determination of the abnormality of the driver and toincrease accuracy of the prediction used for the determination of theabnormality.

As an aspect of the driver state determination apparatus, in the casewhere it is recognized that at least one of the high voluntary functionand the low voluntary function does not work normally, an actuationmethod by an actuation execution section that executes actuation for thedriver may be changed.

Just as described, when the driver is notified or warned, it is possibleto increase the accuracy of the determination of the abnormality of thedriver and to urge the driver to act safely.

As it has been described so far, according to the technique disclosedherein, it is possible to accelerate the determination of theabnormality of the driver.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a conceptual view for illustrating a driver-led automateddriving system.

FIG. 2 is a graph illustrating a breakdown of illnesses and aninfluences on driving.

FIG. 3 is a chart illustrating temporal changes in a decline in a driverstate after an outbreak of the illness.

FIG. 4 is a chart for illustrating ex-post detection and detection ofpredictions.

FIG. 5A is a block diagram illustrating a functional configuration of anautomotive arithmetic system.

FIG. 5B is a block diagram illustrating the functional configuration ofthe automotive arithmetic system.

FIG. 6 is a flowchart illustrating exemplary operation of a driver statedetermination apparatus.

FIG. 7 is a table for illustrating the operation of the driver statedetermination apparatus.

FIG. 8 is a block diagram of computer-based circuitry that may be usedto implement control features of the present disclosure.

DETAILED DESCRIPTION

Merits of a technique of the present disclosure are fully appreciatedparticularly when such a technique is adopted for the second type ofautomated driving system, i.e., an automated driving system with anassumption that a person drives an automobile (hereinafter referred toas a driver-led automated driving system).

Fully-automated driving promises freedom of mobility of elders and is anextremely important advancement. Meanwhile, embodiments disclosed hereinconsiders that the fully-automated driving is not the only automateddriving system (technology) useful for next generation.

The present applicant considers that, with an idea centered on howpersons should be, realization of life fulfilled by free mobility is anideal of a next-generation personal car for which automated drivingtechnique is adopted. In other words, the present applicant considersthat a technique for improving mobility and capability of a person whileproviding pleasure to help the person to be back on a natural humanstate is requested. The present applicant also considers thatrevitalization of mind and a body of the person by enjoyable automobiledriving is a natural effect of the automobile. When driving, the personexerts his/her capability and feels active while the automobile isprepared for appropriate handling of a state by grasping motion ofitself, surrounding environment, and the like, that is, stays present toa sense of the person. As a result, the person in the automobilenaturally senses motion of the automobile and feels safe in theautomobile without a sense of discomfort. When the person owns such anautomobile and has such “fulfilled satisfaction” that the person wantsto drive the automobile as long as possible, the person obtains extremejoyfulness of driving. Meanwhile, the automobile provides the personwith safety and a sense of ease. This is an idea of safety considered bythe present applicant, and this is how the automated driving techniqueby the present applicant is used.

FIG. 1 is a view that schematically illustrates the above overview.

As illustrated in FIG. 1, a driver recognizes travel environmentinformation of the automobile through five senses including eyesight andhearing. The driver determines how to maneuver the automobile on thebasis of the recognized travel environment information, and operates asteering wheel, an accelerator, a brake pedal, or the like on the basisof a determination result. For example, in the case where the driverdetermines a possibility of a collision with an external object ordeparture from a lane, the driver decides a travel route (for example, atarget location, acceleration/deceleration, or the like) to avoid such asituation, and performs an operation therefor. Consequently, variousactuators AC (see FIG. 5B) in the automobile are started, and operationthereof is reflected to travel of the automobile. The driver accepts avehicle travel state as a result of such an operation as feedback, makesa determination on the basis of the feedback and the recognition resultof the travel environment information, and performs an operation on thebasis of the determination. The driver repeats such maneuvers. Here, thetravel environment information is information on travel environment ofthe automobile and includes external environment information of a hostvehicle. For example, the external environment information includesinformation on a sign and a building, information on stationary objectsincluding road information such as a median strip and a center poll, andinformation on mobile objects such as another vehicle (an automatedfour-wheeled vehicle), an automated two-wheeled vehicle, a bicycle, anda pedestrian. Examples of the actuators AC include an engine system 81,a brake system 82, a steering system 83, and a transmission (see FIG.5B).

In regard to the automobile on which the driver-led automated drivingsystem is mounted, the driver usually maneuvers the automobile (in anormal state). In the normal state, the driver-led automated drivingsystem grasps states of the host vehicle and the external environmentand the state of the driver so as to execute virtual driving behindmaneuvering by the driver. In other words, the driver-led automateddriving system is operated as a backup system. More specifically,similar to the driver, the driver-led automated driving systemrecognizes the travel environment information, and also recognizes thestate of the host vehicle and the state of the driver. Based on theabove recognition results, the driver-led automated driving systemdetermines how to move the automobile, and decides target motion of theautomobile in parallel with maneuvering by the driver. Then, whendetermining that the driver suffers from dysfunction or the illness, thedriver-led automated driving system operates the host vehicle instead ofthe driver so as to secure safety of the host vehicle and thesurrounding environment, and also complements a declined function of thedriver among functions such as perception, determination, and operation.

The driver-led automated driving system is designed to be operated asdescribed above as a precondition. Thus, it is extremely important todiscover the outbreak of the abnormality, such as the declined function,the dysfunction, or the illness, to the driver (hereinafter referred toas driver abnormality) as soon as possible.

The driver state is largely categorized into a normal healthy state andan abnormal state where the driver suffers from the dysfunction or theillness. The normal state includes a flow state where the driver isconcentrated on driving at a maximum, a concentrated driving state, arelaxed driving state, an inattentive driving state, a desultory drivingstate, a declined awakening state, and a drowsy state in a descendingorder of a degree of awareness. As discussed in Non-Patent document 1illustrated in FIG. 2, representative examples of the illnesses thatcause conscious disturbance while driving are epilepsy, apoplexy,myocardial infarction, and hypoglycemia.

The inventor has determined that a change of a driver state from thenormal state (the healthy state) to a state where the driver can nolonger drive due to the conscious disturbance may be categorized intothree patterns. FIG. 3 illustrates the three patterns (cases) of adecline in the driver state. In FIG. 3, a vertical axis represents achange in drivability with respect to time on a horizontal axis. In FIG.3, when a pattern falls below a driving incapable line, the driver isassumed to be no longer able to drive.

In FIG. 3, a case A is a pattern that, when the driver suffers from theillness, the driver becomes unconscious without prior warning. The caseA in approximately 55% of all cases. The case A is a pattern that isfrequently observed in cases of epilepsy, and is also observed in somecases of myocardial infarction and a brain disorder. A case B is apattern that the driver does not lose consciousness all at once. In thecase B, the driver state is gradually declined as a whole while thedriver feels something is wrong, that is, a relatively good state and abad state repeatedly occur. The case B occurs at a rate of approximately22% of all the cases and is frequently observed in the cases of a braindisorder. A case C is a pattern that, when the driver suffers from theillness, the driver state is slowly and gradually declined, and thedriver eventually loses consciousness. The case C occurs at a rate ofapproximately 23% of all the cases and is frequently observed in casesof hypoglycemia.

For all of these three cases, with a current technical standard fordetecting the driver state in a level adopted for the normal automobile,the outbreak of the illness can only be recognized after the driverstate reaches the driving incapable line. This is because it isdifficult to determine whether the driver is in a state of being healthybut looming, a state with accumulated fatigue, or a state with theoutbreak of the illness. In addition, even when the driver is in thehealthy state, the driver may close his/her eyes, and a posture of thedriver may become imbalance. Thus, it is necessary to determine theabnormality of the driver under a condition that a state of imbalanceposture and a state of the driver who keeps his/her eyes closed or opencontinue for a specified time. That is, the related art has a problem ofrequiring a long time for determination when it is attempted toaccurately discover the outbreak of the illness, and also has a problemof increased frequency of an erroneous determination when it isattempted to reduce the time required for the determination of theillness.

In view of the above, the inventor focused on a mechanism of loss of thefunctions of the driver in a sequential order. In other words, theinventor paid attention to a prediction of the outbreak of the illness,and considered to determine the outbreak of the driver abnormality byusing a detection result of such a prediction. As a result, it ispossible to promptly and accurately determine the outbreak of the driverabnormality.

In general, as a method for determining the driver abnormality, such atechnique has been known to detect the outbreak of the abnormality of afunction that is established regardless of the driver's intention, thatis, an involuntary function. As a method for detecting the abnormalityof the involuntary function, such a method has been known to analyze theimbalance driving posture of the driver and the eye-opening amount ofthe driver on the basis of a video captured by an imaging section, so asto determine the outbreak of the illness of the driver. When focusing onbrain functions, on contrary to the involuntary function, a voluntaryfunction of a person exists. Recently, techniques of detecting thisvoluntary function have been developed individually. The inventor of thepresent application further investigated classification based on thisvoluntary function while focusing on the brain functions. Morespecifically, the inventor classified the voluntary function into: avoluntary function that was similar to a function of maintaining lifeand was processed in a barely conscious area (herein referred to as a“low voluntary function”); and a voluntary function that was processedin a conscious area (hereinafter referred to as a “high voluntaryfunction”) in comparison with the low voluntary function. For example,the high voluntary function is a function that has an impact on whetherso-called “driving while forecasting possible occurrence of something”can be performed. With such classification, the inventor acquiredknowledge that, after the outbreak of the driver abnormality, the highvoluntary function was likely to be lost first, and the low voluntaryfunction was likely to remain to the last.

FIG. 4 is a conceptual chart illustrating when and what types of thepredictions and events occur in relation to the driver abnormality thatshows behaviors as indicated in the cases B, C in FIG. 3 and alsoillustrating a relationship between each of the predictions and theevents and a life-saving rate at a time point when the prediction or theevent occurs. In FIG. 4, time goes back in a right direction of a timeaxis from timing at which the driver loses consciousness.

In a right portion, FIG. 4 illustrates examples of the predictions(hereinafter referred to a few-minute advance predictions) that aredetected a few minutes before the driver abnormality occurs and thedriver becomes no longer able to drive. In the few-minute advancepredictions, particularly, relatively high conscious behavior of thedriver, that is, the high voluntary function is likely to be changed.Thus, in order to detect onset of the few-minute prediction, it isfocused on whether the high voluntary function works normally. Thechange in the high voluntary function is detected on the basis ofindices that include a saccade reaction against saliency and frequencyof rapid operations, for example.

More specifically, for example, as the few-minute advance predictions ofdriving action, a cognitive function and a motor function tend todecline. For example, as the few-minute advance prediction of eyemovement, trackability of eyes tend to be declined. For example, as thefew-minute advance prediction of the driving posture, the motor functiontends to be declined. For example, as the few-minute advance predictionsof a biological reaction, an internal state and vital signs of thedriver tend to become abnormal, which causes an abnormal heart rate ofthe driver. Thus, by determining the abnormality of the vital sign orthe decline in any of the various functions described above, it ispossible to detect the few-minute advance prediction. In order to detectthe few-minute advance prediction, for example, a vehicle maneuvermodel, a driving operation model, a tracking eye movement model, acooperative action model, and the like can be used. All of thefew-minute advance predictions do not always occur, and only one or someof the few-minute advance predictions may occur. An example of aspecific detection method of the few-minute advance prediction will bedescribed below. At a stage of the few-minute advance prediction, thedriver may be in a state capable of continuing driving. At the stage ofthe few-minute advance prediction, the life-saving rate is approximately85%.

In a center portion, FIG. 4 illustrates examples of the predictions(hereinafter referred to 10-second advance predictions) that aredetected several seconds or a dozen seconds before the driverabnormality occurs and the driver becomes no longer able to drive. Inthe 10-second advance prediction, substantially unconscious behavior ofthe driver is likely to be changed. That is, at a stage of the 10-secondadvance prediction at which a certain time elapses from the outbreak ofthe driver abnormality and which is a relatively close stage to thedriving incapable line, in addition to the high voluntary function, thelow voluntary function is also likely to be changed. Thus, in order todetect the onset of the 10-second advance prediction, whether the lowvoluntary function works normally is determined. The detection ofwhether the low voluntary function works normally can be made on thebasis of indices that include vestibulo-ocular reflex, constancy of ahead, wobbling of steering, and stability of a speed, for example. Forexample, the 10-second advance prediction can be detected by using areflective eye movement model, an upper limb posture control model, ahead natural vibration model, a detection result of vehicle behavior,and the like.

More specifically, in the 10-second advance prediction, for example, inthe case where vestibulo-ocular reflex action becomes abnormal, thedriver is likely not to be able to keep his/her eyesight at a fixedposition (for example, to the front) with his/her head rocking. Inaddition, in the 10-second advance prediction, for example, theconstancy of the head tends to become abnormal. As the abnormality ofthe constancy of the head, for example, when the automobile rocks, theconstancy of the head cannot be maintained, and the head rocks more thannecessary. In addition, in a case of muscle stiffing, motion of the headwith respect to rocking of the automobile is significantly reduced incomparison with that of a healthy person. In the 10-second advanceprediction, for example, steering tends to wobble, or the speed tends tobecome unstable. Thus, the 10-second advance prediction can be detectedon the basis of the change in the state of head or the eyes of thedriver or on the basis of the change in the behavior of the automobile.All of the 10-second advance predictions do not always occur, and onlyone or some of the 10-second advance predictions may occur. An exampleof a specific detection method of the 10-second advance prediction willbe described below. At the stage of the 10-second advance prediction,the life-saving rate is approximately 63%.

In a left portion, FIG. 4 illustrates an example in which loss ofconsciousness of the driver is detected ex-post facto (hereinafterreferred to as ex-post detection). As described above, the ex-postdetection can be made on the basis of the imbalance driving posture ofthe driver, a long-term eye closing state/continuity of the eye openingstate, absence of a pupil reaction, or the like. While normal driving,the imbalance driving posture of the driver or eye closing/eye openingtemporarily also occurs. Thus, in the ex-post detection, the abnormalityis usually determined on the basis of whether such a state continues fora specified time. For this reason, there is a problem of an elongatedtime being required to determine the abnormality. For example, a deviantposture model, an image capturing result of the driver by a camera, orthe like can be used for the ex-post detection.

FIG. 5A and FIG. 5B are block diagrams, each of which illustrates afunctional configuration example of a driver-led automated drivingsystem control unit (CU) that has a driver state determination apparatusaccording to an embodiment. In the following description, FIG. 5A andFIG. 5B will collectively be referred to as FIG. 5.

The driver-led automated driving system CU according to the presentdisclosure (hereinafter simply referred to as an automated drivingsystem CU) is functionally divided into a cognitive system block B1, adetermination system block B2, and an operation system block B3.Optionally, the automated driving system CU may include a processor 835and other circuitry in system 800 of FIG. 8, which may be implemented asa single processor-based system, or a distributed processor basedsystem, including remote processing, such as cloud based processing.

The cognitive system block B1 is configured to perceive externalenvironment and internal environment of the automobile (including thedriver state). The determination system block B2 is configured todetermine various states, various situations, and the like on the basisof a recognition result by the cognitive system block B1, so as todetermine the operation of the automobile. The operation system block B3is configured to specifically generate signals/data to be transmitted tothe actuators on the basis of the determination in the determinationsystem block B2.

The automated driving system CU includes: (1) a primary arithmeticsection 200 that includes the cognitive system block B1, thedetermination system block B2, and the operation system block B3 toimplement automated driving during normal driving; and (2) a safetyfunction section 300 that has a function to complement the cognitivesystem block B1 and the determination system block B2 in the primaryarithmetic section 200.

The automated driving system CU receives data as an input signal frominformation acquisition means 10 that acquires information on theinternal/external environment of the automobile. The automated drivingsystem CU may also receive information as an input signal from a systemor a service connected to an external network (for example, the Internetor the like) like cloud computing (described as “EXTERNAL INPUT” in FIG.5).

Examples of the information acquisition means 10 are: (1) plural vehicleexterior cameras 11, each of which is provided to a body or the like ofan automobile 1 to capture an image of the exterior environment of theautomobile; (2) plural radars 12, each of which is provided to the bodyor the like of the automobile 1 to detect an object, a sign, and thelike on the outside of the vehicle; (3) a location sensor 13 thatincludes a positioning system such as the GPS; (4) external input 14from the external network described above or the like; (5) a mechanicalsensor 15 that is attached to the automobile 1; (6) a driver inputsection 16 that accepts an input operation by the driver; and (7) aninternal camera 17 that is provided to a rear-view mirror, a dashboard,or the like of the automobile 1. The external cameras 11 may include animage sensor that takes fixed and/or moving images in the visualspectrum and/or non-visual ranges such as infrared and ultraviolet. Theradars 12 may include short-range radars, SRR, that operate, e.g., inthe 20 GHz to 27 GHz range, long range radars, LRR, operating, e.g., inthe 76 to 81 GHz range, as well as Lidar that operates in at least oneof ultraviolet, visible, and near infrared spectrums using lasers havinga principle wavelength, for example, in a range of 500 nm to 1000 nm.The external input may include navigation data. The internal camera 17may capture images of the posture, facial expression, the eye openingstate, and a line of sight of the driver, the internal environment ofthe automobile, and the like. The mechanical sensor 15 includes avehicle speed sensor that detects an absolute speed of the automobile 1.The driver input section 16 includes a sensor that detects an operationof any of various operation targets, such as an accelerator pedal, abrake pedal, the steering wheel, or various switches, by the driver. Thedriver input section 16 includes, for example: an accelerator operationamount sensor that detects a depression amount of the accelerator pedal;a steering angle sensor that detects a rotation angle (a steering angle)of the steering wheel; and a brake sensor (a hydraulic pressure sensor)that detects a depression amount of the brake pedal. The driver inputsection 16 may also include biological sensors to monitor vital signs ofthe driver, e.g., heart rate.

-   -   —1-1. Primary Arithmetic Section (1)—

A description will herein be made on a configuration of the primaryarithmetic section 200 with exemplary creation of a route using deeplearning by the primary arithmetic section 200.

The cognitive system block B1 and the determination system block B2 inthe primary arithmetic section 200 execute processing by using any ofthe various models that are developed by deep leaning using a neuralnetwork. When the processing is executed by using such a model, drivingmay be controlled on the basis of a comprehensive determination on thevehicle state, the external environment of the automobile, the driverstate, and the like. That is, driving may be controlled by synchronizinga large volume of the input information in real time.

More specifically, the primary arithmetic section 200 includes an objectrecognition section 201 that recognizes an external object, a mapcreation section 202, an external environment estimation section 203, anexternal environment model 204, a route search section 205, a routecreation section 206, and a vehicle state detection section 207.

The object recognition section 201 receives the image (including thevideo) of the outside of the automobile that is captured by the externalcamera 11 and recognizes the external object on the basis of thereceived image. A recognition result of the object recognition section201 is transmitted to the map creation section 202.

The map creation section 202 divides a surrounding area of the hostvehicle into plural areas (for example, front, right, left, and rear),and executes processing to create a map of each of the areas. Morespecifically, the map creation section 202 integrates target informationrecognized by the external camera 11 and object information recognizedby the radar 12, and reflects the integrated information to the map ofeach of the areas.

The map created by the map creation section 202 and a detection resultof the vehicle state detection section 207 are used when the externalenvironment estimation section 203 estimates the exterior environment ofthe automobile in image recognition processing using deep learning. Morespecifically, the external environment estimation section 203 executesthe image recognition processing that is based on the externalenvironment model 204 developed by using deep learning, and creates a 3Dmap that represents the exterior environment. In deep learning, a deepneural network (DNN) is used. As the DNN, for example, a convolutionalneural network (CNN) is available.

More specifically, in the external environment estimation section 203,(1) the maps of the areas are joined to create an integrated map thatshows surroundings of the host vehicle, (2) changes in a distancebetween the host vehicle and a mobile object in the integrated map aswell as changes in a direction and a relative speed of the mobile objectare predicted, and (3) results thereof are embedded in the externalenvironment model 204. Further specifically, in the external environmentestimation section 203, (4) a location of the host vehicle on theintegrated map is estimated from a combination of high-precision mapinformation that is acquired from inside or outside of the automobile,location information acquired by the GPS or the like, vehicle speedinformation, and six-axis information, (5) cost for the above-describedroute is calculated, and (6) a result thereof and motion information ofthe host vehicle that is acquired from the various sensors are embeddedin the external environment model 204. With the above processing (1) to(6), the external environment estimation section 203 updates theexternal environment model 204 as needed. The external environment model204 is used when the route creation section 206, which will be describedbelow, creates the route.

The signal from the positioning system such as the GPS in the locationsensor 13 and car navigation data from the external network of theexternal input 14, for example, are transmitted to the route searchsection 205. The route search section 205 searches for an extensiveroute for the vehicle by using the signal from the positioning systemsuch as the GPS of the location sensor 13 and the navigation data fromthe external network of the external input 14, for example.

The route creation section 206 creates the travel route of the vehicleon the basis of the above-described external environment model 204 andthe output of the route search section 205. For example, the routecreation section 206 scores safety, fuel economy, and the like, andcreates at least one travel route with a low score. Alternatively, theroute creation section 206 may create the travel route that is based onplural perspectives such as the above travel route and a travel routethat is adjusted according to the operation amount by the driver.Information on the travel route that is created by this route creationsection 206 is included in external environment data.

—1-2. Safety Function Section—

A description will herein be made on a configuration of the safetyfunction section 300 with exemplary creation of a route based on a ruleby the safety function section 300.

The safety function section 300 has a function of assuming a possibilitythat a determination or processing (hereinafter simply referred to asdeviant processing) departing from a particular allowable range isderived from deep learning in the primary arithmetic section 200 andmonitoring such deviant processing.

For example, the safety function section 300 is configured to

(1) recognize the external object (hereinafter may be referred to as atarget object) on the basis of a method for recognizing the target thatis conventionally adopted for the automobile and the like, and(2) set a safe area through which the vehicle can travel safely by usinga method that is conventionally adopted for the automobile and the like,and set a route that runs through such a safe area as the travel routeon which the automobile should travel.

More specifically, the safety function section 300 includes an objectrecognition section 301 that recognizes the external object, aclassification section 302, a preprocessing section 303, a free spacesearch section 304, and a route creation section 305.

The object recognition section 301 recognizes the external object on thebasis of: the image (including the video) of the outside of the vehiclethat is captured by the external camera 11; and a peak list of reflectedwaves detected by the radar 12.

The classification section 302 and the preprocessing section 303 may notexecute deep learning or the like, but may estimate the externalenvironment by a rule-based method based on a specified rule on thebasis of a recognition result of the object recognition section 301.More specifically, the classification section 302 receives the objectrecognition result from the object recognition section 301, andclassifies the recognized objects as one of a mobile object and astationary object. Further specifically, in the classification section302, (1) the surroundings of the host vehicle are divided into theplural areas (for example, the front, the right, the left, and therear), (2) object information recognized by the external camera 11 andobject information recognized by the radar 12 are integrated for each ofthe areas, and (3) classified information of the mobile object and thestationary object in each of the areas is generated.

The preprocessing section 303 integrates a classification result foreach of the areas that is generated in the classification section 302.The integrated information is managed, for example, as theclassification information of the mobile object and the stationaryobject around the host vehicle, on a grid map, and the like. Inaddition, the preprocessing section 303 predicts a distance between themobile object and the host vehicle as well as a direction and a relativespeed of the mobile object, and integrates results thereof as attachedinformation of the mobile object. Furthermore, the preprocessing section303 combines the high-precision map information that is acquired fromthe inside or the outside of the automobile, the location information,the vehicle speed information, the six-axis information, and the like toestimate the location of the host vehicle with respect to the mobileobject/the stationary object.

The free space search section 304 searches for the free space where acollision with the mobile object/the stationary object (hereinafter alsoreferred to as the target object), the location of which is estimated bythe preprocessing section 303, can be avoided. For example, the freespace search section 304 sets the free space on the basis of such aspecified rule that an area located a few meters from the target objectis regarded as an unavoidable area. In the case where the target objectis the mobile object, the free space search section 304 sets the freespace in consideration of a movement speed. For example, the free spaceis an area that is on a road and where a mobile obstacle such as anothervehicle or the pedestrian and a stationary obstacle such as the medianstrip or the center poll do not exist. The free space may include aspace on a road shoulder where an emergency vehicle can be parked.

The route creation section 305 calculates such a route that runs throughthe free space searched by the free space search section 304. Although acalculation method of the route by the route creation section 305 is notparticularly limited, for example, the plural routes running through thefree space are created, and the route with the lowest route cost isselected from the plural routes. The route that is calculated by theroute creation section 305 is output to a target motion decision section214, which will be described later.

The functions of the safety function section 300 that have beendescribed above are implemented by setting rules of the recognitionmethod of the target and an avoidance method thereof, which areconventionally adopted for the automobile and the like.

—1-3. Primary Arithmetic Section (2)—

In addition to the block described in above “1-1. Primary arithmeticsection (1)”, the primary arithmetic section 200 includes a danger statedetermination section 210, a first vehicle model 211, a second vehiclemodel 212, a route decision section 213, the target motion decisionsection 214, a vehicle motion energy setting section 215, an energymanagement section 216, a driver operation recognition section 217, animage processing section 218, and a selector 220. The image processingsection 218 executes specified image processing on the image that iscaptured by the internal camera 17.

In the case where the danger state determination section 210 determinesthat the collision with the target object or the departure from the lanepossibly occurs on the basis of the external environment model 204, thedanger state determination section 210 sets a travel route (for example,the target location and the vehicle speed) to avoid the collision withthe target object or the departure from the lane.

The driver operation recognition section 217 recognizes the operationamount and an operation direction by the driver as information used todecide the travel route. More specifically, the driver operationrecognition section 217 recognizes the operation amount and theoperation direction by the driver on the basis of the output of thedriver input section 16, and outputs a recognition result to the routedecision section 213.

The route decision section 213 decides the vehicle travel route on thebasis of the travel route set by the route creation section 206, thetravel route set by the route creation section 305 in the safetyfunction section 300, and the recognition result of the driver operationrecognition section 217. Although a method for deciding this travelroute is not particularly limited, for example, during the normaltravel, the route decision section 213 may make the travel route set bythe route creation section 206 a top priority. Alternatively, in thecase where the travel route set by the route creation section 206 doesnot run through the free space searched by the free space search section304, the route decision section 213 may select the travel route set bythe route creation section 305 in the safety function section 300.Further alternatively, according to the operation amount or theoperation direction by the driver, the route decision section 213 mayadjust the selected travel route or prioritizes the operation by thedriver.

The target motion decision section 214 decides target six-axis motion(for example, acceleration, an angular speed, and the like) with respectto the travel route decided by the route decision section 213, forexample. When deciding the target six-axis motion, the target motiondecision section 214 may use the specified first vehicle model 211. Asix-axis vehicle model is created by modeling acceleration in three-axisdirections of “front-rear”, “right-left”, and “up-down” of the travelingvehicle and angular speeds in three-axis directions of “pitch”, “roll”,and “yaw”. That is, instead of acknowledging motion of the vehicle on aplane (only in the front-rear/right-left direction (movement in X-Y) andyaw motion (Z-axis) of the vehicle), which has been practiced in thetraditional vehicle motion engineering, the six-axis vehicle model iscreated as a numerical model that replicates behavior of the vehicle byusing a total of the six axes that include pitching (Y-axis), rolling(X-axis) motion, and movement in the Z-axis (vertical motion of avehicle body) of a vehicle body placed on four wheels via suspensions.The first vehicle model 211 is created on the basis of a basic motorfunction of the vehicle, which is set in advance, the interior/exteriorenvironment information of the vehicle, and the like, for example, andis appropriately updated.

The vehicle motion energy setting section 215 calculates torque that isrequested to each of a drive system, a steering system, and a brakesystem in regard to the target six-axis motion decided by the targetmotion decision section 214. For example, the drive system includes anengine system, a motor, and the transmission. For example, the steeringsystem includes the steering wheel. For example, the brake systemincludes the brake.

The energy management section 216 calculates a control amount of theactuators AC to obtain the highest energy efficiency at the time whenthe target motion decided by the target motion decision section 214 isachieved. More specifically exemplified, the energy management section216 calculates opening/closing timing of intake/exhaust valves, fuelinjection timing of an injector, and the like that improve the fueleconomy the most in order to achieve energy torque decided by the targetmotion decision section 214. When performing energy management, theenergy management section 216 may use the specified second vehicle model212. The second vehicle model 212 is a model that represents energyconsumption of the vehicle. More specifically, the second vehicle model212 is a model that represents the fuel economy and electric consumptionfor the operation of the actuators AC in the vehicle. In detail, thesecond vehicle model 212 is created by modeling the opening/closingtiming of the intake/exhaust valves, the fuel injection timing of theinjector, valve opening/closing timing of an exhaust recirculationsystem, and the like that improve the fuel economy the most when aspecified amount of the engine torque is output, for example. The secondvehicle model is created during the travel of the vehicle, for example,and is appropriately updated.

As described above, in this embodiment, the automated driving system CUacknowledges the host vehicle, the external environment, and the driverstate so as to execute the virtual driving in parallel with driving ofthe automobile by the driver. In the case where the automated drivingsystem CU acknowledges the driver state and determines that thedysfunction or the illness has occurred to the driver, the automateddriving system CU actuates the actuators AC for operating the automobileon the basis of the output from the vehicle motion energy settingsection 215 and the energy management section 216 described above.

Such switching operation can be realized by the above-describedconfiguration for automated driving, a driver state recognition section400, an automated driving switching section 410, and the selector 220.

The driver state recognition section 400 recognizes the driver state onthe basis of the information acquired by the information acquisitionmeans 10, the processing result in each of the above-described blocks,and the like. Then, the driver state recognition section 400acknowledges the states of the high voluntary function, the lowvoluntary function, and the involuntary function on the basis of therecognition result. For example, the driver state recognition section400 recognizes the driver state on the basis of the results recognizedby the object recognition sections 201, 301 and the driver operationrecognition section 217, the image capturing information by the internalcamera 17 (the image processing result of the image processing section218), the output information from the external environment estimationsection 203, and/or the like. In FIG. 5B, the driver state recognitionsection 400 receives the output from the preprocessing section 303, thedriver operation recognition section 217, the image processing section218, and the external environment estimation section 203. However,embodiments are not limited thereto. The driver state recognitionsection 400 may recognize the states of the high voluntary function, thelow voluntary function, and the involuntary function on the basis of theinformation from other sections.

As illustrated in FIG. 5B, the driver state recognition section 400includes a high voluntary function recognition section 401, a lowvoluntary function recognition section 402, and an involuntary functionrecognition section 403.

The high voluntary function recognition section 401 recognizes whetherthe high voluntary function of the driver works normally. Morespecifically, the high voluntary function recognition section 401 checkswhether the operation based on the high voluntary function of the driveris performed on the basis of the external environment estimated by theexternal environment estimation section 203, an operating status of theaccelerator pedal or the steering wheel recognized by the driveroperation recognition section 217, and the output from the mechanicalsensor 15, for example. Further specifically, the high voluntaryfunction recognition section 401 recognizes how the driver acts when thedriver passes a location such as a corner or an intersection where aperson possibly and suddenly runs in front of the driver. As a result ofa demonstration experiment, the inventor has acquired such knowledgethat the driver decelerates the vehicle to secure safety as approachingthe corner or the intersection in the case where the high voluntaryfunction works normally, that is, in the healthy state. In contrast, thedriver drives through the corner or the intersection withoutdeceleration in the case where the driver suffers from a symptom ofapoplexy and thus an anticipation function of the driver is lost.According to the knowledge acquired by the inventor, the driver in sucha state may travel on an usually empty road by tracing a lane, e.g.,following lane lines. Traveling by tracing the lane relates to thevoluntary function that can be processed even when close tounconsciousness, and belongs to the low voluntary function in theclassification by the inventor. In other words, the low voluntaryfunction may work normally even when the high voluntary function hasdeclined.

Accordingly, the high voluntary function recognition section 401determines whether the driving operation that corresponds to thelocation where the driver passes through, in particular, the drivingaction with the assumption of a prediction of danger, so-called “drivingwhile forecasting possible occurrence of something” is executed normallyon the basis of the recognition result and/or the detection result ofthe external environment estimation section 203, the driver operationrecognition section 217, and/or the mechanical sensor 15 describedabove. Whether a travel environment is an environment that requires theautomobile to perform “driving while forecasting possible occurrence ofsomething”, may be determined, e.g., by quantifying map information (forexample, the target information such as building information) acquiredfrom the GPS or the like and travel risk information of each of thetargets. Then, the high voluntary function recognition section 401recognizes that the high voluntary function does not work normally inthe case where the driver continues performing rapid operations such asrapid depression of the accelerator pedal and rapid turning of thesteering wheel regardless of a fact that the travel risk is increased.Alternatively, the high voluntary function recognition section 401 mayrecognize whether the high voluntary function works normally on thebasis of motion of the line of sight of the driver such as the saccadereaction against saliency or movement of the line of sight to a figureif present. Further alternatively, with the assumption of combinationwith the other indices, the high voluntary function recognition section401 may count the number of checking of an internal camera mirror, andmay use the extremely small number of checking of the mirror or asignificant reduction in the number for the recognition of the highvoluntary function. In addition, as described above, in a situationwhere the high voluntary function does not work normally, changes mayoccur to an autonomic system of the driver. Thus, such an autonomicsystem (e.g., a heart rate) may be monitored in order to confirm achange in the high voluntary function.

In general, it is more difficult to recognize the high voluntaryfunction than the low voluntary function and the involuntary function.Accordingly, the high voluntary function recognition section 401 (1) mayrecognize that the high voluntary function does not work normally bycombining plural indices, or (2) may cause an actuation executionsection 50 to execute actuation for the driver so as to check reactionof the driver and recognize that the high voluntary function does notwork normally on the basis of the result in the case where it isestimated that the high voluntary function does not work normally.Although a configuration of the actuation execution section 50 is notparticularly limited, a dedicated device may be provided therefor.Alternatively, a screen of a car navigation system or a head-up displayor a sound producing device such as a horn or a speaker may be used. Thehigh voluntary function recognition section 401 is an example of thevoluntary function recognition section.

The low voluntary function recognition section 402 recognizes whetherthe low voluntary function works normally. More specifically, the lowvoluntary function recognition section 402 acknowledges presence orabsence of wobbling of steering or instability of the speed from theresults recognized by the object recognition sections 201, 301 and thedriver operation recognition section 217, for example, so as to checkwhether the operation based on the low voluntary function of the driveris performed. Further specifically, for example, the low voluntaryfunction recognition section 402 checks whether the travel of theautomobile on the lane wobbles on the basis of the results recognized bythe object recognition sections 201, 301 and the driver operationrecognition section 217. The low voluntary function recognition section402 also recognizes whether the driver performs such travel that adistance between the host vehicle and a forward vehicle is repeatedlyreduced and increased. Furthermore, as described above, the lowvoluntary function recognition section 402 checks whethervestibulo-ocular reflex is normal, that is, whether the constancy of thehead is maintained on the basis of the image capturing result of theinternal camera 17. For example, the low voluntary function recognitionsection 402 analyzes the motion of the head of the driver and motion ofthe line of sight of the driver on the basis of the video captured bythe internal camera 17, so as to check whether vestibulo-ocular reflexis normal. Moreover, the low voluntary function recognition section 402analyzes a degree of rocking of the head of the driver with respect torocking of the automobile on the basis of the output from the mechanicalsensor 15 and the video captured by the internal camera 17, so as tocheck whether the constancy of the head is maintained. The low voluntaryfunction recognition section 402 is an example of the voluntary functionrecognition section.

The involuntary function recognition section 403 recognizes whether theinvoluntary function works normally on the basis of a fact that theinvoluntary function remains in the abnormal state for a specified timeor longer. For example, the involuntary function recognition section 403analyzes the imbalance driving posture of the driver and the eye openingamount of the driver on the basis of the image capturing result of theinternal camera 17, so as to determine the outbreak of the illness ofthe driver. More specifically, the involuntary function recognitionsection 403 recognizes the outbreak of the illness of the driver, forexample, in the case where an imbalance state of the driving posture ofthe driver and/or the eye opening/closing state of the driver continuesfor more than a predetermined period of time, e.g., two seconds.

When recognizing the outbreak of the illness of the driver, the driverstate recognition section 400 outputs a recognition result to theautomated driving switching section 410.

The automated driving switching section 410 outputs a control signal tothe selector 220 on the basis of the output from the driver staterecognition section 400.

The selector 220 has a function of switching whether or not to actuallytransmit the control signal for the virtual driving, which is calculatedby the automated driving system CU, to the actuators AC. The selector220 receives the control signals for actuating the actuators AC from thevehicle motion energy setting section 215 and the energy managementsection 216. The selector 220 is configured not to output the controlsignal for the virtual driving during the normal operation, that is, inthe case where the driver is in the state capable of driving normally.Meanwhile, in the case where the driver state recognition section 400recognizes the abnormality such as the illness of the driver, theselector 220 receives the control signal from the automated drivingswitching section 410 and outputs the control signal (hereinafterreferred to as an automated driving control signal) for actuating theactuators AC from the vehicle motion energy setting section 215 and theenergy management section 216. In each of the actuators AC (including anECU that operates the actuators), when the automated driving controlsignal is output from the selector 220, automated driving that is basedon the automated driving control signal is adopted instead of drivingbased on the operation signal from the driver.

As described above, according to the configuration in this embodiment,in the case where the driver state recognition section 400 recognizesthe outbreak of the illness of the driver, the automated drivingswitching section 410 is notified of the recognition result, and theautomated driving switching section 410 controls the selector 220. Inthis way, it is possible to operate the host vehicle instead of thedriver in the manner to secure the safety of the host vehicle and thesurroundings and to complement the declined function among the functionsof the perception, the determination, and the operation of the driver.

Here, the technique of the present application is characterized in apoint of reducing a determination time for the final driver abnormalitydetermination by using the driver state recognition section 400 todetect the high voluntary function, the low voluntary function, and theinvoluntary function of the driver during driving and by combining thenormal/abnormal determinations of the functions.

A specific description will hereinafter be made with reference to FIGS.6 and 7. FIG. 6 is a flowchart illustrating an example of the operationof the driver state determination apparatus, and FIG. 7 is a table forillustrating the operation of the driver state determination apparatus.

As illustrated in FIG. 3, there is a case where the illness of thedriver is shifted stepwise such that the abnormality is found in thehigh voluntary function as Phase 1, the abnormality is found in the lowvoluntary function as Phase 2, and the abnormality is found in theinvoluntary function as Phase 3. There is also a case where theabnormality is found in the involuntary function immediately after theoutbreak of the illness of the driver. Furthermore, there is a casewhere the abnormality is found in the low voluntary function at firstand then the abnormality is found in the involuntary function.

For the above reason, in FIG. 6, the abnormality determination of thehigh voluntary function (Phase 1), the abnormality determination of thelow voluntary function (Phase 2), and the abnormality determination ofthe involuntary function (Phase 3) are processed in parallel.

In step S11, it is determined whether the high voluntary function worksnormally. In step S21, it is determined whether the low voluntaryfunction works normally. In step S31, it is determined whether theinvoluntary function works normally.

Case 1 in FIG. 7 illustrates an example of the case where it isrecognized that the high voluntary function works normally and that thelow voluntary function works normally. That is, Case 1 in FIG. 7illustrates the example of the case where the determinations in step S11and step S21 are OK. At this time, for a determination in step S31,settings in a default state are used. For example, in the case where theabnormal state of the involuntary function continues for a specifieddetermination time, the involuntary function recognition section 403determines that the involuntary function is abnormal. Although thespecified determination time is not particularly limited, an example inwhich the specified determination time is two seconds is illustrated inFIG. 7. In the case of the configuration illustrated in FIG. 5B, whenthe driver state recognition section 400 recognizes the outbreak of theillness of the driver, the recognition result is output to the automateddriving switching section 410. Then, the automated driving switchingsection 410 controls the selector 220 and sends the output of theautomated driving system that has executed virtual driving to theactuators AC, so as to switch to automated driving.

Case 2 in FIG. 7 illustrates an example of the case where it isrecognized that the high voluntary function works normally and that thelow voluntary function does not work normally. That is, Case 2 in FIG. 7illustrates the example of the case where the determination in step S11is OK and the determination in step S21 is NG. At this time, theinvoluntary function recognition section 403 reduces the determinationtime that is required to recognize whether the involuntary functionworks normally. An example in which the determination time is reducedfrom two seconds to one second is illustrated in FIG. 7. In this way, inthe case where the abnormality of the involuntary function of the drivercontinues for one second, the involuntary function recognition section403 determines that the involuntary function is abnormal.

Case 3 in FIG. 7 illustrates an example of the case where it isrecognized that the high voluntary function does not work normally. Thatis, Case 3 in FIG. 7 illustrates the example of the case where thedetermination in step S11 is NG. In this case, in step S12, for example,the low voluntary function recognition section 402 changes adetermination condition used to determine whether the low voluntaryfunction works normally. In an example illustrated in FIG. 7, the lowvoluntary function recognition section 402 reduces a threshold used todetermine the abnormality of the low voluntary function so as to promotethe determination of the abnormality. Furthermore, if the determinationin step S21 is NG, in step S22, the involuntary function recognitionsection 403 reduces the determination time that is required to recognizewhether the involuntary function works normally. An example in which thedetermination time is reduced to 0.5 second is illustrated in FIG. 7. Ifthe determination in step S11 is NG, the determination conditions may bechanged for both of the low voluntary function recognition section 402and the involuntary function recognition section 403. For example, thethreshold for the low voluntary function recognition section 402 may bereduced, and the determination time by the involuntary functionrecognition section 403 may be reduced to one second. Here, the changeof the determination condition for the abnormality of the low voluntaryfunction is not limited to the reduction of the threshold. For example,accuracy of the determination criterion or the determination thresholdused to detect the abnormality of the driver may be increased. In thisway, it is possible to substantially reduce the time required for theabnormality determination and to increase the accuracy of theabnormality determination.

As it has been described so far, according to this embodiment, thevoluntary function recognition sections (the high voluntary functionrecognition section 401 and the low voluntary function recognitionsection 402) are provided to each recognize whether the voluntaryfunction works normally. Then, in the case where it is recognized thatthe voluntary function does not work normally, the determination timethat is required to determine whether the involuntary functionrecognition section functions normally is reduced. That is, in thisembodiment, the voluntary function recognition section detects theprediction before the outbreak of the driver abnormality. Then, in thecase where the prediction is detected, the determination time that isrequired to determine whether the involuntary function recognitionsection functions normally is reduced. In this way, even in the casewhere the driving function of the driver is impaired due to the illnessor the like, the driver abnormality determination can be accelerated.Therefore, it is possible to promptly and reliably execute safetycontrol such as automated driving and automated stop.

In this embodiment, such knowledge is acquired that, in the case wherethe voluntary function is classified into the high voluntary functionand the low voluntary function, after the outbreak of the driverabnormality, the high voluntary function is lost first, and the lowvoluntary function remains to the end. Thus, before the involuntaryfunction is declined, the condition of the abnormality determination ischanged, and the determination time is reduced according to thecombination of normality/abnormality of the high voluntary function andthe low voluntary function. As a result, it is possible to furtheraccelerate the driver abnormality determination and to increase theaccuracy of the prediction related to the abnormality determination.

In the present disclosure, in addition to a concept that the timerequired for the determination is directly reduced as illustrated inFIG. 7, the reduction of the time includes a concept that the timerequired for the abnormality determination is indirectly reduced byrelaxing the threshold for the abnormality determination or increasingthe accuracy of the determination threshold.

OTHER EMBODIMENTS

In the above embodiment, in the case where the determinations in stepsS11 and S21 are NG, in steps S12 and S22, the determination condition(for example, the determination time) for each of the functions isthereafter changed. However, a step of resetting this changeddetermination condition may be provided. For example, in the case wherethe normal state is maintained for the specified time after thedetermination in step S11 and/or step S21 is NG, for example, in thecase where a state with the determination of OK is maintained in stepsS11, S21, and S31, the processing may proceed to step S40, and thedetermination condition for each of the functions may be reset to adefault value.

In the above embodiment, in the flow illustrated in FIG. 6, theautomated driving system CU may ask the driver a question such as “Areyou OK?” or “Why don't you have a short break?” before and afterchanging the determination condition, and may change the processingaccording to the result. For example, in the case where the automateddriving system CU asks the above question and receives a quick andappropriate response from the driver, the automated driving system CUmay not change the determination condition in step S12 and/or step S22.

In this way, when the actuation such as asking the driver the questionor warning the driver, it is possible to increase the accuracy of thedriver abnormality determination and urge the driver to act safely.

The following description relates to a computer environment in whichembodiments of the present disclosure may be implemented. Thisenvironment may include an embedded computer environment, localmulti-processor embodiment, remote (e.g., cloud-based) environment, or amixture of all the environments.

FIG. 8 illustrates a block diagram of a computer that may implement thevarious embodiments described herein. The present disclosure may beembodied as a system, a method, and/or a computer program product. Thecomputer program product may include a computer readable storage mediumon which computer readable program instructions are recorded that maycause one or more processors to carry out aspects of the embodiment.

The non-transitory computer readable storage medium may be a tangibledevice that can store instructions for use by an instruction executiondevice (processor). The computer readable storage medium may be, forexample, but is not limited to, an electronic storage device, a magneticstorage device, an optical storage device, an electromagnetic storagedevice, a semiconductor storage device, or any appropriate combinationof these devices. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes each of the following (andappropriate combinations): flexible disk, hard disk, solid-state drive(SSD), random access memory (RAM), read-only memory (ROM), erasableprogrammable read-only memory (EPROM or Flash), static random accessmemory (SRAM), compact disc (CD or CD-ROM), digital versatile disk (DVD)and memory card or stick. A computer readable storage medium, as used inthis disclosure, is not to be construed as being transitory signals perse, such as radio waves or other freely propagating electromagneticwaves, electromagnetic waves propagating through a waveguide or othertransmission media (e.g., light pulses passing through a fiber-opticcable), or electrical signals transmitted through a wire.

Computer readable program instructions described in this disclosure canbe downloaded to an appropriate computing or processing device from acomputer readable storage medium or to an external computer or externalstorage device via a global network (i.e., the Internet), a local areanetwork, a wide area network and/or a wireless network. The network mayinclude copper transmission wires, optical communication fibers,wireless transmission, routers, firewalls, switches, gateway computersand/or edge servers. A network adapter card or network interface in eachcomputing or processing device may receive computer readable programinstructions from the network and forward the computer readable programinstructions for storage in a computer readable storage medium withinthe computing or processing device.

Computer readable program instructions for carrying out operations ofthe present disclosure may include machine language instructions and/ormicrocode, which may be compiled or interpreted from source code writtenin any combination of one or more programming languages, includingassembly language, Basic, Fortran, Java, Python, R, C, C++, C# orsimilar programming languages. The computer readable programinstructions may execute entirely on a user's personal computer,notebook computer, tablet, or smartphone, entirely on a remote computeror compute server, or any combination of these computing devices. Theremote computer or compute server may be connected to the user's deviceor devices through a computer network, including a local area network ora wide area network, or a global network (i.e., the Internet). In someembodiments, electronic circuitry including, for example, programmablelogic circuitry, field-programmable gate arrays (FPGA), or programmablelogic arrays (PLA) may execute the computer readable programinstructions by using information from the computer readable programinstructions to configure or customize the electronic circuitry, inorder to perform aspects of the present disclosure.

Aspects of the present disclosure are described herein with reference toflow diagrams and block diagrams of methods, apparatus (systems), andcomputer program products according to embodiments of the disclosure. Itwill be understood by those skilled in the art that each block of theflow diagrams and block diagrams, and combinations of blocks in the flowdiagrams and block diagrams, can be implemented by computer readableprogram instructions.

The computer readable program instructions that may implement thesystems and methods described in this disclosure may be provided to oneor more processors (and/or one or more cores within a processor) of ageneral purpose computer, special purpose computer, or otherprogrammable apparatus to produce a machine, such that the instructions,which execute via the processor of the computer or other programmableapparatus, create a system for implementing the functions specified inthe flow diagrams and block diagrams in the present disclosure. Thesecomputer readable program instructions may also be stored in a computerreadable storage medium that can direct a computer, a programmableapparatus, and/or other devices to function in a particular manner, suchthat the computer readable storage medium having stored instructions isan article of manufacture including instructions which implement aspectsof the functions specified in the flow diagrams and block diagrams inthe present disclosure.

The computer readable program instructions may also be loaded onto acomputer, other programmable apparatus, or other device to cause aseries of operational steps to be performed on the computer, otherprogrammable apparatus or other device to produce a computer implementedprocess, such that the instructions which execute on the computer, otherprogrammable apparatus, or other device implement the functionsspecified in the flow diagrams and block diagrams in the presentdisclosure.

FIG. 8 is a functional block diagram illustrating a networked system 800of one or more networked computers and servers. In an embodiment, thehardware and software environment illustrated in FIG. 8 may provide anexemplary platform for implementation of the software and/or methodsaccording to the present disclosure.

Referring to FIG. 8, a networked system 800 may include, but is notlimited to, computer 805, network 810, remote computer 815, web server820, cloud storage server 825 and computer server 830. In someembodiments, multiple instances of one or more of the functional blocksillustrated in FIG. 8 may be employed.Additional detail of computer 805 is shown in FIG. 8. The functionalblocks illustrated within computer 805 are provided only to establishexemplary functionality and are not intended to be exhaustive. And whiledetails are not provided for remote computer 815, web server 820, cloudstorage server 825 and compute server 830, these other computers anddevices may include similar functionality to that shown for computer805.

Computer 805 may be built into the automobile, a personal computer (PC),a desktop computer, laptop computer, tablet computer, netbook computer,a personal digital assistant (PDA), a smart phone, or any otherprogrammable electronic device capable of communicating with otherdevices on network 810.

Computer 805 may include processor 835, bus 837, memory 840,non-volatile storage 845, network interface 850, peripheral interface855 and display interface 865. Each of these functions may beimplemented, in some embodiments, as individual electronic subsystems(integrated circuit chip or combination of chips and associateddevices), or, in other embodiments, some combination of functions may beimplemented on a single chip (sometimes called a system on chip or SoC).

Processor 835 may be one or more single or multi-chip microprocessors,such as those designed and/or manufactured by Intel Corporation,Advanced Micro Devices, Inc. (AMD), Arm Holdings (Arm), Apple Computer,etc. Examples of microprocessors include Celeron, Pentium, Core i3, Corei5 and Core i7 from Intel Corporation; Opteron, Phenom, Athlon, Turionand Ryzen from AMD; and Cortex-A, Cortex-R and Cortex-M from Arm.

Bus 837 may be a proprietary or industry standard high-speed parallel orserial peripheral interconnect bus, such as ISA, PCI, PCI Express(PCI-e), AGP, and the like.

Memory 840 and non-volatile storage 845 may be computer-readable storagemedia. Memory 840 may include any suitable volatile storage devices suchas Dynamic Random Access Memory (DRAM) and Static Random Access Memory(SRAM). Non-volatile storage 845 may include one or more of thefollowing: flexible disk, hard disk, solid-state drive (SSD), read-onlymemory (ROM), erasable programmable read-only memory (EPROM or Flash),compact disc (CD or CD-ROM), digital versatile disk (DVD) and memorycard or stick.

Program 848 may be a collection of machine readable instructions and/ordata that is stored in non-volatile storage 845 and is used to create,manage and control certain software functions that are discussed indetail elsewhere in the present disclosure and illustrated in thedrawings. In some embodiments, memory 840 may be considerably fasterthan non-volatile storage 845. In such embodiments, program 848 may betransferred from non-volatile storage 845 to memory 840 prior toexecution by processor 835.

Computer 805 may be capable of communicating and interacting with othercomputers via network 810 through network interface 850. Network 810 maybe, for example, a local area network (LAN), a wide area network (WAN)such as the Internet, or a combination of the two, and may includewired, wireless, or fiber optic connections. In general, network 810 canbe any combination of connections and protocols that supportcommunications between two or more computers and related devices.

Peripheral interface 855 may allow for input and output of data withother devices that may be connected locally with computer 805. Forexample, peripheral interface 855 may provide a connection to externaldevices 860. External devices 860 may include input devices, e.g., anyor all of the devices in the information acquisition means 10 and/orother suitable input devices, and output devices, e.g., any or all ofthe various actuator devices AC and/or other suitable output devices,e.g., a speaker. External devices 860 may also include portablecomputer-readable storage media such as, for example, thumb drives,portable optical or magnetic disks, and memory cards. Software and dataused to practice embodiments of the present disclosure, for example,program 848, may be stored on such portable computer-readable storagemedia. In such embodiments, software may be loaded onto non-volatilestorage 845 or, alternatively, directly into memory 840 via peripheralinterface 855. Peripheral interface 855 may use an industry standardconnection, such as RS-232 or Universal Serial Bus (USB), to connectwith external devices 860.

Display interface 865 may connect computer 805 to display 870, e.g., ahead-up display or a screen of a car navigation system. Display 870 maybe used, in some embodiments, to present a command line or graphicaluser interface to a user of computer 805. Display interface 865 mayconnect to display 870 using one or more proprietary or industrystandard connections, such as VGA, DVI, DisplayPort and HDMI.

As described above, network interface 850, provides for communicationswith other computing and storage systems or devices external to computer805. Software programs and data discussed herein may be downloaded from,for example, remote computer 815, web server 820, cloud storage server825 and compute server 830 to non-volatile storage 845 through networkinterface 850 and network 810. Furthermore, the systems and methodsdescribed in this disclosure may be executed by one or more computersconnected to computer 805 through network interface 850 and network 810.For example, in some embodiments the systems and methods described inthis disclosure may be executed by remote computer 815, computer server830, or a combination of the interconnected computers on network 810.

Data, datasets and/or databases employed in embodiments of the systemsand methods described in this disclosure may be stored and or downloadedfrom remote computer 815, web server 820, cloud storage server 825 andcompute server 830.

1. A driver state determination apparatus for use in an automobile, thedriver state determination apparatus comprising: circuitry configuredto: recognize whether a voluntary function of the driver works normallyto predict occurrence of an abnormality of the driver; and recognizethat an involuntary function of the driver does not work normally oncondition that the involuntary function remains in an abnormal state forat least a specified time, wherein on condition that the voluntaryfunction does not work normally, reduce the specified time required torecognize whether the involuntary function works normally.
 2. The driverstate determination apparatus according to claim 1, wherein thecircuitry is further configured to: recognize whether a first voluntaryfunction of the driver works normally; and recognize whether a secondvoluntary function of the driver, which is a lower voluntary functionthan the first voluntary function, works normally, on condition that thefirst voluntary function is not working normally, change determinationcriteria for recognition of the second voluntary function and/or thespecified time for the involuntary function recognition, and oncondition that the second voluntary function does not work normally,change the specified time for the involuntary function recognition. 3.The driver state determination apparatus according to claim 2, whereinthe circuitry is further configured to: estimate an externalenvironment; recognize an operating status of actuators in the vehicle;determine a speed of the automobile; and check whether the driverdecelerates the vehicle when approaching a corner or an intersectionbased on the external environment, the operating status and the speed ofthe automobile, wherein on condition that one of these conditions is notmet, determine that the first voluntary function is not workingnormally.
 4. The driver state determination apparatus according to claim2, wherein the circuitry is further configured to: check whethersteering of the automobile is wobbling or whether speed of theautomobile is unstable, wherein, on condition that one of theseconditions is met, determine that the second voluntary function is notworking normally.
 5. The driver state determination apparatus accordingto claim 2, wherein the circuitry is further configured to: determinewhether vestibulo-ocular reflex is normal, and, if not, determine thatthe second voluntary function is not working normally.
 6. The driverstate determination apparatus according to claim 2, wherein thecircuitry is further configured to” on condition that the firstvoluntary function is not working normally, reduce a threshold used todetermine the abnormality of the second voluntary function.
 7. Thedriver state determination apparatus according to claim 2, wherein thecircuitry is further configured to: on condition that at least one ofthe first voluntary function and the second voluntary function does notwork normally, output automated driving control signals to actuators inthe vehicle.
 8. The driver state determination apparatus according toclaim 7, wherein the circuitry is further configured to, beforeoutputting automated driving control signals, orally or visually givethe driver a warning.
 9. The driver state determination apparatusaccording to claim 8, on condition that the driver makes an appropriateresponse to the warning, the circuitry is further configured to refrainfrom outputting automated driving control signals.
 10. The driver statedetermination apparatus according to claim 7, wherein the circuitry isfurther configured to, after outputting control signals, orally orvisually give the driver a warning.
 11. The driver state determinationapparatus according to claim 2, wherein the specified time for theinvoluntary function recognition when the first voluntary function isnot working normally is less than when the only second voluntaryfunction is not working normally.
 12. A driver state controldetermination method for a driver in a vehicle, the method comprising:recognizing whether a voluntary function of the driver works normally topredict occurrence of an abnormality of the driver; and recognizing thatan involuntary function of the driver does not work normally oncondition that the involuntary function remains in an abnormal state forat least a specified time, wherein on condition that the voluntaryfunction does not work normally, reducing the specified time required torecognize whether the involuntary function works normally.
 13. Themethod according to claim 12, further comprising: recognizing whether afirst voluntary function of the driver works normally; and recognizingwhether a second voluntary function of the driver, which is a lowervoluntary function than the first voluntary function, works normally, oncondition that the first voluntary function is not working normally,changing determination criteria for recognition of the second voluntaryfunction and/or the specified time for the involuntary functionrecognition, and on condition that the second voluntary function doesnot work normally, changing the specified time for the involuntaryfunction recognition.
 14. The method according to claim 13, furthercomprising: on condition that at least one of the first voluntaryfunction and the second voluntary function does not work normally,output automated driving control signals to actuators in the vehicle.15. A non-transitory computer readable storage including computerreadable instructions that when executed by a controller cause thecontroller to execute a driver state determination method for a driverin a vehicle, the method comprising: recognizing whether a voluntaryfunction of the driver works normally to predict occurrence of anabnormality of the driver; and recognizing that an involuntary functionof the driver does not work normally on condition that the involuntaryfunction remains in an abnormal state for at least a specified time,wherein on condition that the voluntary function does not work normally,reducing the specified time required to recognize whether theinvoluntary function works normally.
 16. The method according to claim15, further comprising: recognizing whether a first voluntary functionof the driver works normally; and recognizing whether a second voluntaryfunction of the driver, which is a lower voluntary function than thefirst voluntary function, works normally, on condition that the firstvoluntary function is not working normally, changing determinationcriteria for recognition of the second voluntary function and/or thespecified time for the involuntary function recognition, and oncondition that the second voluntary function does not work normally,changing the specified time for the involuntary function recognition.17. The method according to claim 16, further comprising: on conditionthat at least one of the first voluntary function and the secondvoluntary function does not work normally, output automated drivingcontrol signals to actuators in the vehicle.